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A validation of models for prediction of pathogenic variants in mismatch repair genes

  • Cathy Shyr
  • , Amanda L. Blackford
  • , Theodore Huang
  • , Jianfeng Ke
  • , Nofal Ouardaoui
  • , Lorenzo Trippa
  • , Sapna Syngal
  • , Chinedu Ukaegbu
  • , Hajime Uno
  • , Khedoudja Nafa
  • , Zsofia K. Stadler
  • , Kenneth Offit
  • , Christopher I. Amos
  • , Patrick M. Lynch
  • , Sining Chen
  • , Francis M. Giardiello
  • , Daniel D. Buchanan
  • , John L. Hopper
  • , Mark A. Jenkins
  • , Melissa C. Southey
  • Aung Ko Win, Jane C. Figueiredo, Danielle Braun, Giovanni Parmigiani

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Purpose: Models used to predict the probability of an individual having a pathogenic homozygous or heterozygous variant in a mismatch repair gene, such as MMRpro, are widely used. Recently, MMRpro was updated with new colorectal cancer penetrance estimates. The purpose of this study was to evaluate the predictive performance of MMRpro and other models for individuals with a family history of colorectal cancer. Methods: We performed a validation study of 4 models, Leiden, MMRpredict, PREMM5, and MMRpro, using 784 members of clinic-based families from the United States. Predicted probabilities were compared with germline testing results and evaluated for discrimination, calibration, and predictive accuracy. We analyzed several strategies to combine models and improve predictive performance. Results: MMRpro with additional tumor information (MMRpro+) and PREMM5 outperformed the other models in discrimination and predictive accuracy. MMRpro+ was the best calibrated with an observed to expected ratio of 0.98 (95% CI = 0.89-1.08). The combination models showed improvement over PREMM5 and performed similar to MMRpro+. Conclusion: MMRpro+ and PREMM5 performed well in predicting the probability of having a pathogenic homozygous or heterozygous variant in a mismatch repair gene. They serve as useful clinical decision tools for identifying individuals who would benefit greatly from screening and prevention strategies.

Original languageEnglish
Pages (from-to)2155-2166
Number of pages12
JournalGenetics in Medicine
Volume24
Issue number10
DOIs
Publication statusPublished - Oct 2022

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Colorectal cancer
  • Lynch syndrome
  • Mismatch repair
  • Model combination
  • Model validation

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